Katalog Plus
Bibliothek der Frankfurt UAS
Bald neuer Katalog: sichern Sie sich schon vorab Ihre persönlichen Merklisten im Nutzerkonto: Anleitung.
Dieses Ergebnis aus BASE kann Gästen nicht angezeigt werden.  Login für vollen Zugriff.

Joint spatiotemporal modelling reveals seasonally dynamic patterns of Japanese encephalitis vector abundance across India

Title: Joint spatiotemporal modelling reveals seasonally dynamic patterns of Japanese encephalitis vector abundance across India
Authors: Lydia HV Franklinos; David W Redding; Tim CD Lucas; Rory Gibb; Ibrahim Abubakar; Kate E Jones
Publication Year: 2022
Collection: University of Leicester: Figshare
Subject Terms: Biological sciences; Biomedical and clinical sciences; Health sciences; Animals; Culex; Encephalitis Virus; Japanese; Encephalitis; Bayes Theorem; Seasons; India; Mosquito Vectors
Description: Predicting vector abundance and seasonality, key components of mosquito-borne disease (MBD) hazard, is essential to determine hotspots of MBD risk and target interventions effectively. Japanese encephalitis (JE), an important MBD, is a leading cause of viral encephalopathy in Asia with 100,000 cases estimated annually, but data on the principal vector Culex tritaeniorhynchus is lacking. We developed a Bayesian joint-likelihood model that combined information from available vector occurrence and abundance data to predict seasonal vector abundance for C. tritaeniorhynchus (a constituent of JE hazard) across India, as well as examining the environmental drivers of these patterns. Using data collated from 57 locations from 24 studies, we find distinct seasonal and spatial patterns of JE vector abundance influenced by climatic and land use factors. Lagged precipitation, temperature and land use intensity metrics for rice crop cultivation were the main drivers of vector abundance, independent of seasonal, or spatial variation. The inclusion of environmental factors and a seasonal term improved model prediction accuracy (mean absolute error [MAE] for random cross validation = 0.48) compared to a baseline model representative of static hazard predictions (MAE = 0.95), signalling the importance of seasonal environmental conditions in predicting JE vector abundance. Vector abundance varied widely across India with high abundance predicted in northern, north-eastern, eastern, and southern regions, although this ranged from seasonal (e.g., Uttar Pradesh, West Bengal) to perennial (e.g., Assam, Tamil Nadu). One-month lagged predicted vector abundance was a significant predictor of JE outbreaks (odds ratio 2.45, 95% confidence interval: 1.52–4.08), highlighting the possible development of vector abundance as a proxy for JE hazard. We demonstrate a novel approach that leverages information from sparse vector surveillance data to predict seasonal vector abundance–a key component of JE hazard–over large spatial scales, providing ...
Document Type: article in journal/newspaper
Language: unknown
Relation: 2381/26369932.v1; https://figshare.com/articles/journal_contribution/Joint_spatiotemporal_modelling_reveals_seasonally_dynamic_patterns_of_Japanese_encephalitis_vector_abundance_across_India/26369932
Availability: https://figshare.com/articles/journal_contribution/Joint_spatiotemporal_modelling_reveals_seasonally_dynamic_patterns_of_Japanese_encephalitis_vector_abundance_across_India/26369932
Rights: CC BY 4.0
Accession Number: edsbas.6F13B7AD
Database: BASE